: The paper presents the life cycle cost structure of a newly designed office building where the costs of planning, construction, operation and maintenance are analyzed for time periods of 20 and 30 years according to current market prices and reduced to net present value. The results of the life cycle cost analysis show that for a period of 20 years, the shares of initial or capital costs and operation and maintenance costs are approximately equal. For a period of 30 years, the share of capital costs is 42.6%, and the share of operation and maintenance costs is 57.4%, which exceeds capital costs. These results confirm previous research and the significance of the operation and maintenance costs, and indicate that in engineering practice in the design stage it is necessary to analyze and calculate the total costs of the life cycle of buildings for the purpose of evaluating variant solutions

This research aims to reduce dropout rates in higher education by developing a machine learning model to predict churn early, enabling timely interventions. The most important research findings of the thesis are summarized as follow: - The overall dropout rate at the University of Banja Luka, Bosnia and Herzegovina, was nearly half of enrolled students between the 2007/08 and 2018/19 academic years, with rates showing an increase over time. - Half of the student churn occurs within the first year of enrollment, with significantly higher rates in three-year study programs compared to four-year programs. - The results suggest that at least one-third of student churn could potentially be prevented, as it is attributable to institutional factors. - The findings indicate that it is possible to predict student churn at the earliest stages of education, even when using a challenging dataset with missing data and limited pre-academic, academic, and socioeconomic features. - The use of the Histogram-based Gradient Boosting Classifier (HGBC) in the educational data field, for the first time, resulted in an attrition classification accuracy of 75% at the beginning of the first academic year and 83% by the end, outperforming other widely used models. - To validate the effectiveness of HGBC, the model was tested under varying conditions and consistently demonstrated high performance. - The application of pre- and post-hoc interpretability techniques can enhance the credibility and reliability of machine learning models in predicting student discontinuation. - The most significant predictors of churn include gender (female), student cohort, accumulated ECTS credits, scholarship status, age at enrollment, the number of successfully passed courses, program duration, and whether the student attended a Gymnasium. - Certain variables, such as gender (female) and student cohort, consistently maintained a high level of significance across the models over time. - Models with lower performance and quality exhibited a similar ranking of feature importance to those of the best-performing models.

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Aim: Coronary artery disease is a leading cause of death in Croatia, and percutaneous coronary intervention (PCI) has emerged as a significant advancement in its treatment. The facilitation of PCI is performed by an interventional team consisting of an interventional cardiologist, a nurse, and a radiologic technologist. In order to achieve precise and efficient intervention outcomes, the radiologic technologist’s role in operating the fluoroscopy is crucial. This study’s aim was to investigate the attitudes of undergraduate professional radiologic technology students at the Faculty of Health Studies in Rijeka regarding the role of the radiologic technologist within the specialist team during PCI and the adequacy of competencies acquired during university education. Materials and methods : The research included students from all three years of regular undergraduate professional studies in radiologic technology, representing diverse demographic characteristics. Data were collected online through a questionnaire developed in Google Forms and analyzed using the Statistica software program. Results : The majority of respondents view the radiologic technologist as essential in the medical team during PCI, with effective communication being emphasized as a crucial element for successful teamwork. However, respondents express a belief that their previous education has not equipped them with adequate knowledge and skills to function com-petently within a PCI team, indicating a perceived need for additional education and professional training. Conclusion : The study shows that radiologic technology students comprehend the significance of radiologic technologists in the interventional team for PCI procedures. However, they express concerns regarding the insufficiency of knowledge and skills acquired during their studies for proficient work in interventional cardiology laboratories.

Motivation: Quantitative MRI (qMRI) offers sensitive and specific measures to study age-related microstructural changes in the brain. However, models assessing age trajectories in qMRI brain properties are often incomparable among centers. Goal(s): Develop normative models reflecting aging trajectories and assess the impact of bi-centric, non-fully matched protocols in brain aging studies. Approach: Investigating age trajectories in cortical regions using polynomial regression models, focusing on quantitative R1, R2*, and susceptibility mapping (QSM). Results: We validated data harmonization by observing the impact on normative trajectories using bicentric data, where we noted significantly different maturation and aging inflections for R1 and R2* trajectories across cortical regions. Impact: This bi-centric, multi-parameter qMRI study investigates age-dependent variations across cortical regions, offering a valuable reference for subsequent qMRI aging research and emphasizing age effects on the cortical surface.